12 citations · 23 across the 8 of their papers we have counts for
4 papers · 1 filter
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
Haoyang Fang, Shuai Zhang, Yifei Ma +5
Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that e…
Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs
Heng Wang, Changxing Wu
Implicit Discourse Relation Recognition (IDRR) remains a challenging task due to the requirement for deep semantic understanding in the absence of explicit discourse markers. A fur…
Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
Shiwei Tan, Hengyi Wang, Weiyi Qin +3
Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not present in the…